from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D
from keras.models import Model
from keras import backend as K
import scipy as scipy
import numpy as np
import matplotlib.pyplot as plt
from scipy.cluster.vq import whiten

mat = scipy.io.loadmat('bcgdata.mat')
emat = mat['bcgdata']
#whiten(emat)
emat = emat / np.max(emat)
#emat = emat-np.min(emat)

input_img = Input(shape=(64,1))  # adapt this if using `channels_first` image data format
encoded = Dense(16, activation='relu')(input_img)
encoded = Dense(8, activation='relu')(encoded)

decoded = Dense(16, activation='relu')(encoded)
decoded = Dense(1, activation='relu')(decoded)

autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='SGD', loss='mean_squared_error')
autoencoder.summary()

x_train = np.transpose(emat[:,0:50000])
x_train = np.expand_dims(x_train,axis=2)
#x_train = np.reshape(x_train, (x_train.shape[1], 64, 1))
x_test = np.transpose(emat[:,50000:80000])
x_test = np.expand_dims(x_test,axis=2)
#x_test = np.reshape(x_test, (x_test.shape[1], 64, 1))

from keras.callbacks import TensorBoard

autoencoder.fit(x_train, x_train,
                epochs=50,
                batch_size=150,
                shuffle=True,
                validation_data=(x_test, x_test),
                callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])


preds = autoencoder.predict(x_test)
plt.plot(x_test[0:500,1,0])
plt.plot(preds[0:500,1,0])
plt.show()